Embedding new samples via locality-constrained sparse representation for non-linear manifold learning
How to embed the new observations (or samples) into the low-dimensional space is a crucial problem in non-linear manifold learning techniques.This issue can be converted into the problem of finding an accurate mapping that transfers the unseen data samples into an existing manifold.In this paper,a locality-constrained sparse representation algorithm is proposed to deal with the out-of-sample embedding problem for manifold learning.Through taking the data locality information into consideration,the local data structure can be well preserved in our proposed algorithm.To justify the superiority of the proposed method,our approach has been tested on several challenging face datasets and compared with other out-of-sample embedding techniques.The experimental results show that the proposed method can not only achieve the competitive recognition rate than the existing methods,but also save more time than the traditional nonlinear dimensionality reduction methods for the out-of-sample problem.
Non-linear manifold learning Out-of-sample embedding Sparse representation Locality constraint
Liu Yang Yunyan Wei Feng Pan Xiaohui Li
School of Engineering, Mudanjiang Normal University, Mudanjiang, China College of Computer Science and Information Technology,Key Laboratory of Intelligent Information Pro State Grid Dalian Electric Power Supply Company, Dalian, China
国际会议
秦皇岛
英文
5-9
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）